Main

related bits

0

processing priority

4

site type

3 (personal blog or private political site, e.g. Blogspot, Substack, also small blogs on own domains)

review version

11

html import

20 (imported)

Events

first seen date

2024-08-29 20:15:36

expired found date

-

created at

2024-08-29 20:15:36

updated at

2026-01-28 21:00:17

Domain name statistics

length

20

crc

20070

tld

2211

nm parts

0

nm random digits

0

nm rare letters

0

Connections

is subdomain of id

69893241 (blogspot.com)

previous id

0

replaced with id

0

related id

-

dns primary id

0

dns alternative id

0

lifecycle status

0 (unclassified, or currently active)

Subdomains and pages

deleted subdomains

0

page imported products

0

page imported random

0

page imported parking

0

Error counters

count skipped due to recent timeouts on the same server IP

0

count content received but rejected due to 11-799

0

count dns errors

0

count cert errors

0

count timeouts

0

count http 429

0

count http 404

0

count http 403

0

count http 5xx

0

next operation date

-

Server

server bits

server ip

-

Mainpage statistics

mp import status

20

mp rejected date

-

mp saved date

-

mp size orig

47661

mp size raw text

11441

mp inner links count

14

mp inner links status

20 (imported)

Open Graph

title

description

image

site name

author

updated

2026-01-27 09:59:17

raw text

The Lovento-Blog The Lovento-Blog Features, known bugs, user-requests. It's all about Lovento.com Wednesday, March 29, 2006 Multilanguage folksonomy Phew, multilanguage tags are really a challenge. To design a reasonably efficient tagging system is already challenging enough, especially when it comes to generating A. tag clouds and B. related tags. We have optimized point B by keeping a table of cotags along with their valence, i.e., given a tag X and a tag Y we store how many objects are tagged with both X and Y. This only helps to find related tags for one specific tag. Note that this list of cotags is symmetric, so we can save half of the storage. For a tag combos one would need an analoguous table with n-tuples, which would get REALLY huge even though this n-tensor structure is only sparsely filled. And yet I'm ignoring tag clustering which has to be computed offline due to it's high complexity. But apart from the computational effort there are some conceptual issue...

Text analysis

redirect type

35 (location.replace)

block type

0 (no issues)

detected language

1 (English)

category id

Other [en] (231)

index version

2025123101

spam phrases

0

Text statistics

text nonlatin

0

text cyrillic

0

text characters

8864

text words

1929

text unique words

677

text lines

145

text sentences

101

text paragraphs

19

text words per sentence

19

text matched phrases

1

text matched dictionaries

2

RSS

rss status

32 (unknown)

rss found date

2024-09-01 03:26:58

rss size orig

21546

rss items

13

rss spam phrases

0

rss detected language

1 (English)

inbefore feed id

-

inbefore status

0 (new)

Sitemap

sitemap status

40 (completed successful import of reports.txt file to table in_pages)

sitemap review version

2

sitemap urls count

13

sitemap urls adult

0

sitemap filtered products

0

sitemap filtered videos

0

sitemap found date

2024-09-01 01:39:45

sitemap process date

2024-09-01 01:39:47

sitemap first import date

-

sitemap last import date

2026-01-02 20:37:55